Prediction Techniques in Internet of Things (IoT) Environment: A Comparative Study

Socialization and Personalization in Internet of Things (IOT) environment are the current trends in computing research. Most of the research work stresses the importance of predicting the service & providing socialized and personalized services. This paper presents a survey report on different techniques used for predicting user intention in wide variety of IOT based applications like smart mobile, smart television, web mining, weather forecasting, health-care/medical, robotics, road-traffic, educational data mining, natural calamities, retail banking, e-commerce, wireless networks & social networking. As per the survey made the prediction techniques are used for: predicting the application that can be accessed by the mobile user, predicting the next page to be accessed by web user, predicting the users favorite TV program, predicting user navigational patterns and usage needs on websites & also to extract the users browsing behavior, predicting future climate conditions, predicting whether a patient is suffering from a disease, predicting user intention to make implicit and human-like interactions possible by accepting implicit commands, predicting the amount of traffic occurring at a particular location, predicting student performance in schools & colleges, predicting & estimating the frequency of natural calamities occurrences like floods, earthquakes over a long period of time & also to take precautionary measures, predicting & detecting false user trying to make transaction in the name of genuine user, predicting the actions performed by the user to improve the business, predicting & detecting the intruder acting in the network, predicting the mood transition information of the user by using context history, etc. This paper also discusses different techniques like Decision Tree algorithm, Artificial Intelligence and Data Mining based Machine learning techniques, Content and Collaborative based Recommender algorithms used for prediction

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

Understanding a large-scale IPTV network via system logs

Recently, there has been a global trend among the telecommunication industry on the rapid deployment of IPTV (Internet Protocol Television) infrastructure and services. While the industry rushes into the IPTV era, the comprehensive understanding of the status and dynamics of IPTV network lags behind. Filling this gap requires in-depth analysis of large amounts of measurement data across the IPTV network. One type of the data of particular interest is device or system log, which has not been systematically studied before. In this dissertation, we will explore the possibility of utilizing system logs to serve a wide range of IPTV network management purposes including health monitoring, troubleshooting and performance evaluation, etc. In particular, we develop a tool to convert raw router syslogs to meaningful network events. In addition, by analyzing set-top box (STB) logs, we propose a series of models to capture both channel popularity and dynamics, and users' activity on the IPTV network.Ph.D.Committee Chair: Jun Xu; Committee Member: Jia Wang; Committee Member: Mostafa H. Ammar; Committee Member: Nick Feamster; Committee Member: Xiaoli M

Negotiating reality as an approach to intercultural competence

In an increasingly global business environment, managers must interact effectively with people who have different values, behavioral norms, and ways of perceiving reality. Many jobs now entail an international dimension, so the need to develop intercultural competences has taken on a greater importance for more people in business than ever before. Intercultural competence is the ability to recognize and use cultural differences as a resource for learning and for generating effective responses in specific contexts. We conceive of this as negotiating reality. The approach draws on concepts from international management, sociology, crosscultural psychology, action science and conflict resolution. -- In einer zunehmend globalisierten Umwelt müssen Führungskräfte immer häufiger mit Menschen aus anderen Kulturen zusammenzuarbeiten, die unterschiedliche Wertevorstellungen haben, verschiedene Verhaltensnormen pflegen und ihre jeweils eigene Wahrnehmung der Realität haben. Damit steigt der Bedarf an interkultureller Kompetenz, der Fähigkeit, kulturelle Unterschiede als Lernressourcen zu erkennen und für die jeweilige Situation adäquate Lösungen zu erarbeiten und einzusetzen. Dieser Beitrag beschreibt einen innovativen Ansatz, den wir negotiating reality nennen, der sich aus unterschiedlichen Theoriebereichen, u.a. dem internationalen Management, der Soziologie, der interkulturellen Psychologie, und der Konfliktforschung speist.

Activity understanding and unusual event detection in surveillance videos

PhDComputer scientists have made ceaseless efforts to replicate cognitive video understanding abilities of human brains onto autonomous vision systems. As video surveillance cameras become ubiquitous, there is a surge in studies on automated activity understanding and unusual event detection in surveillance videos. Nevertheless, video content analysis in public scenes remained a formidable challenge due to intrinsic difficulties such as severe inter-object occlusion in crowded scene and poor quality of recorded surveillance footage. Moreover, it is nontrivial to achieve robust detection of unusual events, which are rare, ambiguous, and easily confused with noise. This thesis proposes solutions for resolving ambiguous visual observations and overcoming unreliability of conventional activity analysis methods by exploiting multi-camera visual context and human feedback. The thesis first demonstrates the importance of learning visual context for establishing reliable reasoning on observed activity in a camera network. In the proposed approach, a new Cross Canonical Correlation Analysis (xCCA) is formulated to discover and quantify time delayed pairwise correlations of regional activities observed within and across multiple camera views. This thesis shows that learning time delayed pairwise activity correlations offers valuable contextual information for (1) spatial and temporal topology inference of a camera network, (2) robust person re-identification, and (3) accurate activity-based video temporal segmentation. Crucially, in contrast to conventional methods, the proposed approach does not rely on either intra-camera or inter-camera object tracking; it can thus be applied to low-quality surveillance videos featuring severe inter-object occlusions. Second, to detect global unusual event across multiple disjoint cameras, this thesis extends visual context learning from pairwise relationship to global time delayed dependency between regional activities. Specifically, a Time Delayed Probabilistic Graphical Model (TD-PGM) is proposed to model the multi-camera activities and their dependencies. Subtle global unusual events are detected and localised using the model as context-incoherent patterns across multiple camera views. In the model, different nodes represent activities in different decomposed re3 gions from different camera views, and the directed links between nodes encoding time delayed dependencies between activities observed within and across camera views. In order to learn optimised time delayed dependencies in a TD-PGM, a novel two-stage structure learning approach is formulated by combining both constraint-based and scored-searching based structure learning methods. Third, to cope with visual context changes over time, this two-stage structure learning approach is extended to permit tractable incremental update of both TD-PGM parameters and its structure. As opposed to most existing studies that assume static model once learned, the proposed incremental learning allows a model to adapt itself to reflect the changes in the current visual context, such as subtle behaviour drift over time or removal/addition of cameras. Importantly, the incremental structure learning is achieved without either exhaustive search in a large graph structure space or storing all past observations in memory, making the proposed solution memory and time efficient. Forth, an active learning approach is presented to incorporate human feedback for on-line unusual event detection. Contrary to most existing unsupervised methods that perform passive mining for unusual events, the proposed approach automatically requests supervision for critical points to resolve ambiguities of interest, leading to more robust detection of subtle unusual events. The active learning strategy is formulated as a stream-based solution, i.e. it makes decision on-the-fly on whether to request label for each unlabelled sample observed in sequence. It selects adaptively two active learning criteria, namely likelihood criterion and uncertainty criterion to achieve (1) discovery of unknown event classes and (2) refinement of classification boundary. The effectiveness of the proposed approaches is validated using videos captured from busy public scenes such as underground stations and traffic intersections

A Review of Data Mining in Personalized Education: Current Trends and Future Prospects

Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness. The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process. Driven by data mining techniques, it not only benefits students but also provides educators and institutions with tools to craft customized learning experiences. To offer a comprehensive review of recent advancements in personalized educational data mining, this paper focuses on four primary scenarios: educational recommendation, cognitive diagnosis, knowledge tracing, and learning analysis. This paper presents a structured taxonomy for each area, compiles commonly used datasets, and identifies future research directions, emphasizing the role of data mining in enhancing personalized education and paving the way for future exploration and innovation.Comment: 25 pages, 5 figure

Analytics of time management strategies in online learning environments: a novel methodological approach

The emergence of technology-supported education, e.g., blended and online, has changed the global higher education landscape. Importantly, the new learning modes involve more complex tasks and challenging ways of learning that require effective time management and strong self-regulation skills. In this regard, one of the most prevalent theoretical lenses to understand learning processes is Self-Regulated Learning (SRL). In reference to SRL models, time is a major resource in learning. The way learners schedule, plan, and enact tactics and strategies on their learning time could tremendously impact their academic achievement. However, the assessment of how learners make time-related decisions in learning is a daunting task, particularly given its latent nature and inherent autonomous learning capacity. One way to address this problem is to make use of unprecedented volumes of data collected by digital learning environments that are precisely timestamped records of actions that learners take while studying. This thesis presents a set of novel learning analytics methods for detecting and understanding time management strategies based on the analysis of digital trace data collected in online learning environments. First, the thesis proposes a new method to detect time management tactics and strategies using a combination of sequence mining and clustering techniques. The thesis also describes how time management tactics and strategies detected with this method are aligned with an SRL model that is used as a theoretical foundation of this thesis. Second, the thesis introduces a novel learning analytics method for the detection of time management tactics and strategies. This method uses a combination of process mining and clustering techniques followed by a complementary process mining technique that has a unique feature to bring insights into the temporal learning processes. This new method also has a strong potential to inform and enhance understanding of how learners make complex decisions about their learning. Third, the thesis investigates mutual connections between time management and learning strategies and their combined connections with academic performance using epistemic network analysis. This analysis provides empirical evidence that supports the proposition that time management is a critical characteristic of effective self-regulated learners. Fourth, the thesis proposes a novel method that integrates computational and visualization techniques to explore the frequency, connections, ordering, and the time of the execution of time management and learning tactics, which usually been done in isolation in the existing literature. Then, the thesis quantitatively and theoretically compare time management and learning strategies detected with this new method to explore the role of time management and learning strategies in learning as drawing on theories of educational psychology. Fifth, this new method was validated in a study that was conducted on the trace data of different learning modalities and interaction modes, where large cohorts are involved. This final study emphasizes the importance of multivocality approach in the study of time management and other relevant learning constructs. Finally, the thesis concludes with a discussion of practical implications, the significance of the results, and future research directions

Layered evaluation of interactive adaptive systems : framework and formative methods

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